In this paper, we develop an inference framework allowing for fast and robust reconstruction of GISAXS data to accelerate GISAXS data analysis. As the signal-to-noise ratio (SNR) of experimental images might suffer from distortions caused by grazing-incidence geometry [1]}, we mainly focus on using an in-plane scattering signalWe define the in-plane scattering signal (profile) as the average of a central region (see Fig. REF A) of an image over the lateral dimension of a detector with subtracted parasitic scattering signal from the beamstop. as input. Despite the recent success of discriminative neural networks, such models do not account for the inherent ambiguity of reconstruction and, as a rule, do not provide a researcher with uncertainty quantification. Instead of learning a function from images to parameters, we use the Bayesian approach and estimate the posterior distribution of object parameters given the GISAXS data. Our framework has a two-fold structure. In the first step, we learn a robust probabilistic representation of the GISAXS generative process with variational auto-encoders [2]}. Second, we model the posterior distribution via likelihood-free inference [3]} with normalizing flows [4]}.
